Automatic Matching of Full and Degraded Bullet Lands

Heike Hofmann

Automatic Matching of Full and Degraded Bullet Lands







Heike Hofmann, Eric Hare, Alicia Carriquiry
Center for Statistics and Applications in Forensic Evidence
Iowa State University
May 18th, 2017

Outline

x3p format: ISO 25178-72:2017

Automated Matching Algorithm: Front-End Web Application

https://isu-csafe.stat.iastate.edu/shiny/bulletr/

Automated Matching Algorithm

Step 1: extract profile

Automated Matching Algorithm

Step 2: identify shoulders

Automated Matching Algorithm

Step 3: long-pass Gaussiam filter

Automated Matching Algorithm

Step 4: get land signature

Automated Matching Algorithm

Step 5: identify peaks

Automated Matching Algorithm

Step 6: align pairs of lands

Automated Matching Algorithm

Step 6 (cont’d): match peaks across lands

Automated Matching Algorithm

Automated Matching Algorithm: Extract Features

Features are extracted from each land-to-land comparison:

More Features

Reference database

Distribution of Features

Data-driven rules

Algorithm: Forest of 1000 trees

Feature Importance

How much of a land do we need for a match?

Feature Standardization

True Degraded Case

shorter signatures are not penalized (as much) by using standardized features

Here: Matches = 8 (full: 27), Matches per mm = 11.42 (14.72)

Standardized Features

Degraded Bullets

Simulation Study:

  1. Three types of Degradation:
    1. Left Fixed - The left portion of the land (leading shoulder) is recoverable.
    2. Middle Fixed - The middle portion of the land is recoverable.
    3. Right Fixed - The right portion of the land (trailing shoulder) is recoverable.
  2. Six Degradation Levels: 100% (Fully recovered), 87.5% Recovered, 75% Recovered, 62.5% Recovered, 50% Recovered, 37.5% Recovered, 25% Recovered.

Simulation Results

Testing this Finding

To come full circle, we will attempt to extract a 50% degraded signature from a Hamby bullet land with bad tank rash in one half. (Barrel 9 Bullet 2 Land 4):

Br924 Results

Extracting the ideal signature and then simulating a left-fixed 50% degradation scenario yields the following:

Future Work

Reference Database

biggest limitation thus far is limited amount of available 3D scan data for bullets:

in NIST ballistics database (Xiaoyu Alan Zhang, https://tsapps.nist.gov/NRBTD):

11 unique gun barrels is not yet enough to form a true reference distribution for known matches and non-matches…

…However, the structure of the database means that as soon as new data is available, the features and scores can be easily recomputed.

please contribute your experimental data!

Future work

Thank You

Special thanks to Alan Zheng at the National Institute of Standards and Technology for maintaining the NIST Ballistics Toolmark Research Database and providing many useful suggestions.

Announcement

References

Biasotti, Alfred A. 1959. “A Statistical Study of the Individual Characteristics of Fired Bullets.” Journal of Forensic Sciences 4 (1): 34–50.

Chu, Wei, Robert M Thompson, John Song, and Theodore V Vorburger. 2013. “Automatic identification of bullet signatures based on consecutive matching striae (CMS) criteria.” Forensic Science International 231 (1–3): 137–41.

Clarkson, James A, and C Raymond Adams. 1933. “On Definitions of Bounded Variation for Functions of Two Variables.” Transactions of the American Mathematical Society 35 (4). JSTOR: 824–54.

Hamby, James E., David J. Brundage, and James W. Thorpe. 2009. “The Identification of Bullets Fired from 10 Consecutively Rifled 9mm Ruger Pistol Barrels: A Research Project Involving 507 Participants from 20 Countries.” AFTE Journal 41 (2): 99–110.

Vorburger, T.V., J.-F. Song, W. Chu, L. Ma, S.H. Bui, A. Zheng, and T.B. Renegar. 2011. “Applications of Cross-Correlation Functions.” Wear 271 (3–4): 529–33. doi:http://dx.doi.org/10.1016/j.wear.2010.03.030.